KiloBot：一种用于大规模部署感知引导工业机械臂的编程语言
KiloBot: A Programming Language for Deploying Perception-Guided Industrial Manipulators at Scale
AI总结：
本文提出领域特定语言 KiloBot，通过易用的 TAMP 问题构建接口和灵活控制流机制，结合图形化编程前端，使无编程经验的操作员也能大规模部署感知引导的工业机械臂应用。
摘要：
我们希望工业机器人能够借助摄像头和感知流水线处理非结构化环境。与回放离线生成轨迹的传统工业机器人不同，这些感知引导的工业应用需要在线行为规划。除了感知和规划算法之外，部署感知引导的机械臂还需要在集成方面投入大量精力。一种方法是使用传统语言（如 Python）编写脚本，以构建规划问题并与其他算法模块及外部设备进行集成。虽然用 Python 编写脚本对于少量机器人和应用是可行的，但大规模部署感知引导的操作（例如，在 2000 多个客户现场部署超过 10000 个机器人工作站）变得难以实现。为了解决这一挑战，我们提出了一种面向感知引导操作应用的领域特定语言（DSL）。为了扩大部署规模，我们的 DSL 提供：1）一个易于访问的接口，用于构建和求解在实际应用中很重要的一类任务与运动规划（TAMP）问题；2）一种实现灵活控制流的机制，以执行集成并满足不同工业应用的定制化需求。结合直观的图形化编程前端，我们的 DSL 主要由没有传统编程语言编码经验的机器操作员使用。经过数小时的培训，操作员就能够使用我们的 DSL 编排复杂而有趣的操作行为。广泛的实际部署证明了我们方法的有效性。
英文摘要：
We would like industrial robots to handle unstructured environments with cameras and perception pipelines. In contrast to traditional industrial robots that replay offline-crafted trajectories, online behavior planning is required for these perception-guided industrial applications. Aside from perception and planning algorithms, deploying perception-guided manipulators also requires substantial effort in integration. One approach is writing scripts in a traditional language (such as Python) to construct the planning problem and perform integration with other algorithmic modules & external devices. While scripting in Python is feasible for a handful of robots and applications, deploying perception-guided manipulation at scale (e.g., more than 10000 robot workstations in over 2000 customer sites) becomes intractable. To resolve this challenge, we propose a Domain-Specific Language (DSL) for perception-guided manipulation applications. To scale up the deployment,our DSL provides: 1) an easily accessible interface to construct & solve a sub-class of Task and Motion Planning (TAMP) problems that are important in practical applications; and 2) a mechanism to implement flexible control flow to perform integration and address customized requirements of distinct industrial application. Combined with an intuitive graphical programming frontend, our DSL is mainly used by machine operators without coding experience in traditional programming languages. Within hours of training, operators are capable of orchestrating interesting sophisticated manipulation behaviors with our DSL. Extensive practical deployments demonstrate the efficacy of our method.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
